Application phenotyping

ABSTRACT

A collection of techniques is disclosed to allow for the detection of malware that leverages pattern recognition and machine learning to effectively provide “content-less” malware detection, i.e., detecting a process as being an ‘anomaly’ not based on its particular content, but instead based on comparisons of its behavior to known (and characterized) ‘trusted’ application behaviors, i.e., the trusted applications&#39; “phenotypes” and/or the phenotypes of known malware applications. By analyzing the patterns of normal behavior performed by trusted applications as well as malware applications, one can build a set of sophisticated, content-agnostic behavioral models (i.e., “application phenotypes”)—and later compare the processes executed on a user device to the stored behavioral models to determine whether the actual measured behavior reflects a “good” application, or if it differs from the stored behavioral models to a sufficient degree and with a sufficient degree of confidence, thus indicating a potentially malicious application or behavior.

TECHNICAL FIELD

Embodiments described herein generally relate to malware detection and,in particular, to the detection of malware infections (and otheranomalies) via the creation and use of so-called “applicationphenotypes,” as well as various analytic techniques, such as neuralnetworks and machine learning.

BACKGROUND ART

Malware infections on computers and other electronic devices are veryintrusive and hard to detect and repair. In fact, malware and exploitsoften evade detection altogether, forcing constant software updates tothe prevention and detection technologies installed on user devices.Anti-malware solutions may operate by matching a signature of maliciouscode or files against software that has been approved (i.e., a“whitelist”) to determine whether the software is harmful to a computingsystem. However, malware may disguise itself through the use ofpolymorphic programs or executables, wherein the malware changes itselfto avoid detection by anti-malware solutions. In such cases,anti-malware solutions may fail to detect new or morphed malware in azero-day attack. Malware may include, but is not limited to, spyware,rootkits, password stealers, spam, sources of phishing attacks, sourcesof denial-of-service-attacks, viruses, loggers, Trojans, adware, or anyother digital content that produces unwanted activity.

Existing solutions that attempt to perform malware and anomaly detectionusing “whitelisting,” while a simple and efficient way to protect asystem against malware, are often ineffective against exploits and arehighly restrictive—most frequently resulting in ‘binary’ rules to eitherallow or block actions, making it extremely difficult to use in a fluidand customized consumer setup. While detecting and blocking programsthat have been “blacklisted” is achievable, addressing programs that are“gray” (i.e., neither approved nor disapproved) is a growing challengein the field of malware detection.

Thus, what is needed is a system that performs malware (and otheranomaly) detection, leveraging both pattern recognition and machinelearning to effectively provide “content-less” malware detection, i.e.,detecting a process as being an ‘anomaly’ not based on its particularcontent, but instead based purely on comparisons of its behavior toknown (and characterized) ‘normal’ application behaviors, i.e., theapplication's “phenotype.” By analyzing the patterns of normal behaviorcommonly performed by approved applications, one can build a set ofsophisticated, content-agnostic behavioral models (i.e., “applicationphenotypes”) for particular applications—and later compare the processesexecuted on a user device to the stored behavioral models to determinewhether the actual measured behavior differs from the stored behavioralmodels to a sufficient degree and with a sufficient degree ofconfidence, thus indicating a potentially malicious process or behavior.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 is an illustration of an example embodiment of a system forprofiling code execution.

FIG. 2 is a block diagram illustrating exemplary malware microstep rulesand application phenotype logic, according to one embodiment.

FIG. 3 is a diagram illustrating the relationships between exemplaryapplications, application phenotypes, and individual behaviors,according to one embodiment.

FIG. 4 is a block diagram illustrating a computer-implemented system forbuilding and maintaining application phenotypes, according to anotherembodiment.

FIG. 5 is a block diagram illustrating a computer system for detectingmalware or other anomalies, according to another embodiment.

FIG. 6 is a flowchart illustrating a technique for building applicationphenotypes, according to one embodiment.

FIG. 7 is a flowchart illustrating a technique for detecting maliciousbehavior, according to one embodiment.

DESCRIPTION OF EMBODIMENTS

In the following description, for purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the invention. It will be apparent, however, to oneskilled in the art that the invention may be practiced without thesespecific details. In other instances, structure and devices are shown inblock diagram form in order to avoid obscuring the invention. Referencesto numbers without subscripts or suffixes are understood to referenceall instance of subscripts and suffixes corresponding to the referencednumber. Moreover, the language used in this disclosure has been selectedprincipally for readability and instructional purposes, and may not havebeen selected to delineate or circumscribe the inventive subject matter,resort to the claims being necessary to determine such inventive subjectmatter. Reference in the specification to “one embodiment” or to “anembodiment” means that a particular feature, structure, orcharacteristic described in connection with the embodiments is includedin at least one embodiment of the invention, and multiple references to“one embodiment” or “an embodiment” should not be understood asnecessarily all referring to the same embodiment.

The embodiments described herein are examples and for illustrativepurposes. Persons of ordinary skill in the art will recognize thatalternative techniques for implementing the disclosed subject matter maybe used. Elements of example embodiments may be arranged in differentarrangements or combined with elements of different example embodiments.For example, the order of execution of blocks and flow charts may bechanged. Some of the blocks of those flowcharts may be changed,eliminated, or combined and other blocks may be added as desired.

As used herein, the term “a computer system” can refer to a singlecomputer or a plurality of computers working together to perform thefunction described as being performed on or by a computer system.

Increasingly, cyber-attacks on businesses, government agencies, andothers are covert and persistent. As a result, the ability to detectcovert communications becomes increasingly more important to being ableto deal with threats to intellectual property and personal informationtheft. However, as summarized below, many of the techniques employed bytoday's systems to detect malware and other exploits either havesignificant drawbacks or are overly restrictive. Current approaches tomalware detection include the following:

Static Malware Detection:

Compared to static malware detection, the application phenotype is notimpacted by minute alterations to the original sample. This is becauseapplication phenotypes do not rely on signatures.

Behavioral Protection:

Behavioral solutions monitor the actions of an application—but not howand why an action is performed. As an example, behavioral monitoring mayobserve that Internet Explorer (“IE”) wrote a file to disk, but it wouldnot be able to differentiate the scenario where IE was instructed by auser to save a file to disk from the scenario where an exploit tookplace in the context of IE and the malicious payload is now writing afile to disk. “Application Phenotyping,” as described below, would beable to differentiate between the two scenarios—allowing the first one,while blocking the second. Whereas behavioral solutions tend to focus onmalware, application phenotypes focus on “white” applications and theparticular sequence of steps required to be performed for a whiteapplication to perform tasks of interest (e.g., establishing a networkconnection, writing to disk, etc.).

HIPS and Exploit Prevention:

Host Intrusion Prevention Systems (HIPS) and exploit preventionsolutions focus on a set of particular exploit techniques (e.g., bufferoverflow, stack pivot, heap spray, Return-oriented programming (“ROP”),etc.). Application phenotypes would block the exploit attempts bycomparing the sequence of actions that led to the exposed behavior withthe sequences defined in the phenotype. If the current sequence doesn'texist in the phenotype, the behavior would either be flagged or blockedoutright. As an example, when IE writes a file to disk after a ROPexploit, the sequence of actions (e.g., the execution of so-called“gadgets”) that led to the call to the write API would not defined as anaccepted sequence in the original IE phenotype, thus resulting in thespecific malicious write action being blocked.

Whitelisting and Application Control Technologies:

Existing whitelisting technologies enforce a set of binary rules. Whilethey may generate an automatic list of applications and potentialassociated behaviors, they do not consider the so-called “microsteps”(as will be described in detail below) that led to the behavior beingexhibited. For example, a given malware protection program may block allnetwork connection from the Calculator program. “calc.exe” based on apredefined rule, but it will not learn from the usage of calc.exe ifestablishing a network connection is, in actuality, a valid behavior forcalc.exe, and it will not be able to identify the specific sequence ofAPI calls that lead to the network connection being established. In asimilar manner, a “whitelisting” solution would allow IE to establish anetwork connection, irrespective of how the connection is established(e.g., whether it was established during the course of normal browsingbehavior or an unauthorized ROP exploit).

Closed OSes and Application Permissions:

Closed Operating Systems (“OSes”) provide, via the default installprocess of applications, a mechanism to highlight the permissionsrequired by an application to function correctly. The permissionsrequested may, as an example, include access to the network or to thestorage. The permission, while beneficial to restrict PotentiallyHarmful Applications (“PHAs”) would not prevent a clean application thatwas granted network access from establishing a malicious connectionwhile being exploited.

Thus, disclosed herein are various techniques to overcome theshortcomings of static, software-based malware analysis, as well asother existing malware detection methods outline above. Specifically,disclosed herein are systems and methods to implement a novel“application phenotyping” framework to support secure, efficient,dynamic, and content-agnostic analysis of programs and processes todetect malware or other anomalies. This approach solves several of theother common problems with prior art, software-based malware detectiontechniques.

Application Phenotyping: An Introduction

Phenotype, classically, from a biological point of view, refers to thesummary of all the characteristics and behaviors of an organism, definedby its genotype plus the effect of the environment, plus anydevelopmental changes that have happened to the organism over its life.In order to do roughly the same type of characterization with “good” or“approved” applications, such that deviations from these “good” or“approved” application, which are likely to be malicious, may beidentified, the embodiments described herein employ the novel concept of“microsteps.”

In the case of computer applications, it would be impractical anderror-prone to define a phenotype by individual operations performed bythe application (particularly if the operations occur across multiplethreads and/or the sequence of operations is variable). Thus, asdescribed in the embodiments disclosed herein, individual operations (orsequence of APIs) may be aggregated into microsteps, i.e., a high-leveldescription of the intent of the combined operations. An applicationphenotype constructed of sequence(s) of individual microsteps will beeasier to store and compare. It will also be less error-prone and makeit significantly easier to identify known good behaviors and known badbehaviors, as well as optionally forward unknown behaviors to aclassification engine. According to embodiments disclosed herein, then,the malware detection system will look for deviations from the “normal”behavior. The intent is to do this purely heuristically, i.e., in a“content-agnostic” or “content-less” fashion.

Microsteps

As mentioned above, the basic principle of “application phenotyping” isto characterize the behavior of known, good applications using objectsreferred to herein as “microsteps.” Microsteps may be thought of ascollections of behaviors that map onto “logical intent.” In other words,microsteps are essentially a way to ‘up-level’ standard kernel-leveloperations (e.g., files, registry, register keys, processes, networkoperations, etc.) into a high-level concept. Microsteps may alsorepresent the aggregation of individual low-level operations andhigh-level contextual information delivered by various program APIs.Moreover, microsteps may be broken down further into both first-tier andsecond-tier microsteps, as is explained in further detail below.

First-tier Microsteps: First-tier microsteps comprise an aggregation orsequence of different operations on kernel objects that represent ahigher-level function, e.g., a browser that calls a function “InternetCreate Cookie.” Inherent to this higher-level function could be, e.g.,registry activity that allows a process to figure out where the cookiestore is. A process may then call some other APIs to “create cookie,”and drop data in it, etc. Then, all of these processes may need to beclosed down. All in all, this seemingly simple process of creating acookie could represent a sequence of, e.g., twenty operations. Thesetwenty operations could then be “up-leveled” into one microstep that iscreated for a given Internet session.

Second-tier Microsteps: Second-tier microsteps may indicate muchhigher-level operations that are combinations of individual first-tiermicrosteps. E.g., an “Open File Dialog” on Windows has many conceptsinvolved, e.g., registry, I/O, etc., which may include first-tiermicrosteps, such as: “Initialize the shell object name space for MyNetwork Places”; “Determine my current directory”; Send Query; DrawShape; and Close Window, etc. Another example of a second-tier microstepmight be “Instantiate COM Object.” [This second-tier microstep couldpotentially include a much larger number of microsteps, e.g., load up aCOM object with this GUID, determine where it lives on disk, determinean object model, instantiate object, load.dll's, etc.].

The goal, according to some embodiments disclosed herein, then, would beto characterize the behavior of an application by a series of“Second-tier Microsteps.” For example, a system may want to characterize“IE startup” as a collection of second tier microsteps. Or, it may wantto characterize “IE is doing nothing” as a microstep. Either of thesehypothetical second-tier microsteps, alone or in combination could bedeemed to be “normal” behaviors. There could also be “normal” behaviorsdefined based on typical user behaviors, e.g., visiting a website suchas www.google.com could be represented by a typical set of second-tiermicrosteps. According to some embodiments, microsteps must be able totolerate a certain amount of “variance” in the underlying operations andstill resolve to the same pre-identified “microstep.” For example, theorder that events come in and/or the paths of objects that are accessedcould differ slightly from operation to operation, but they should stillbe identifiable as the “same” microstep if they share a sufficientnumber of core characteristics/behaviors. The end result of developingsufficiently robust microsteps, then would be microsteps that could beapplied to any machine in the world, irrespective of how the machine isconfigured, and the monitoring system would be able to determine if theapplication is ‘behaving’ in a normal manner. By looking for the knownoperations, the system may quickly be able to find deviations.

Why Microsteps?

Empirical research reveals that applications are not initiated in a“random” manner. In fact, most applications are executed by following astandard and fairly static pattern. Deviation from said pattern is oftenan anomaly. Empirical research further reveals that applications alsohave a limited number of behaviors. An exhaustive list of all thesebehaviors may be generated, and then any observed behavior that is notin the original list may be considered an anomaly. While users caninfluence the behavior of an application, they cannot change the set offunctions offered by said application. For example, the Calculatorprogram in the “calc.exe” example does not establish networkconnections. No matter what action(s) a user attempts to take with theCalculator program, it should not establish an Internet connection. Inother words, the behavior of “Establishing a network connection” is notpart of the standard application phenotype of “calc.exe,” and,therefore, attempts by such program to perform such behavior shouldlikely be blocked.

Some exploits use so-called “Return-Oriented Programming (ROP) attacks,”i.e., a technique where the malicious program manipulates the stack sothat it returns to a different place from where it originated. ROPattacks were created by malware developers in response to Host IntrusionPrevention Solutions (HIPS), which marked certain pages at ‘notexecutable,’ so that their payload was no longer executable. Tocircumvent this, some malware attempts to chain together small subsetsof already existing instructions (i.e., “gadgets”), and then transfercontrol to their malicious payload. So, the normal path of MicrosoftWord opening a file may involve: determine whether the file is present;determine whether the process has the right to open the file; parsingthe file; defaulting to a “Read Only” mode, and then finally displayingthe file, etc. Typically, a malicious program may just open the fileright away, and may even open the file in a different manner (e.g.,skipping the open in “Read Only” mode step), so there would be manyattributes that differed from the normal opening of the file. A malwaredetection employing microsteps and application phenotypes wouldn'tnecessarily know that such a ROP attack was “bad” per se; it would justbe flagged as “not normal” (i.e., not a chain that we allowedoriginally), and so it would be able to block it or request furtherapproval/investigation from an administrator or user.

As stated above, there are often a limited number of paths that wouldlead to a particular behavior exhibited by a particular application.Thus, by monitoring trigger events and comparing the API sequence thatled to the event to the defined set of approved API calls (mostfrequently involving OS-level APIs), one could block attack likecode/DLL injection or exploits (including ROP and “gadget” chaining). Toconsider a concrete example, while “Establishing a network connection”may be part of the standard application phenotype of Internet Explorer,the way internet explorer establishes these connections follows a knownsequence of OS system calls. When Internet Explorer is the target of anexploit, the sequence of APIs that leads to “Iexplorer.exe” establishinga network connection differs from the known “normal” sequences. Thedifference may be detected, which allows for the behavior to be blockedby the malware prevention system.

Further, while it may be normal for Internet Explorer to send andreceive encrypted data to and from one or more Internet servers, it maynot be normal to send encrypted data and not process a response. Notreceiving a response could, e.g., indicate the presence of malicioussoftware running in the context of Internet Explorer by sending secretsout to a third-party server. By “up-leveling” the individual operationsusing the aforementioned micro-steps, it becomes easier to identify themalicious network behavior microstep: “Send encrypted data.” Inaddition, the process of identification of a malicious microstep may begreatly simplified by removing all known good microsteps from theanalysis process. Whatever microsteps are left over may be considered tobe either “unknown” microsteps or “known bad” microsteps.

Thus, the inventors have realized that each application's phenotype maybe defined by the particular scenarios that led to the application beingexecuted, the behaviors the application exhibits, and the sequence ofmicrosteps undertaken taken to perform the exhibited behaviors. Theconcept of application phenotype ties together many aspects ofapplication's genealogy, behavioral monitoring, and the application'stracing and emulation down to the instruction level.

A Content-less Approach

Another element that distinguishes application phenotypes from theexisting solutions in the field, is the ability to ship the solution ina content-less fashion. In other words, by executing the solution in a“learning mode” on a clean system for some amount of time, the list ofthe normal behaviors and their associated microsteps may be gathered andstored locally before locking down the device and enforcing the recordedphenotypes. In other embodiments, the solution could be shipped in ahybrid fashion, that is, shopped with some set of “normal” behaviors forcommonly-used applications (e.g., MS Office), but then also implement a“learning mode,” since “normal” operation could differ slightly fromsystem to system.

According to some embodiments, the “learning” and “enforcement”processes for application phenotyping may involve the followinggeneralized steps:

1.) Every time a process is launched, it is traced, i.e., “phenotyped.”The process may be “known white,” “known black,” or “gray.” Thephenotype may computed and stored locally, and/or on at enterpriselevel, and/or in the “cloud,” i.e., on a network-accessible server.2.) Each time a new phenotype trace is generated, it may be stored andcompared to the prior results. Using this process, it is possible to“crowd-source” a collection of traces for a given process.3.) If a deviation from the stored phenotype is observed, a newreputation value may be set, along with a confidence score. Thereputation and confidence score may be influenced by both the type ofdeviation from the stored phenotype, as well as the magnitude of thedeviation.4.) In the absence of a direct match, the computed phenotype may becompared to known bad and known good phenotypes. The comparison yields areputation and confidence score. The closer the match, the greater theconfidence that a given behavior is the same as a known phenotype.

Before particular application behaviors may be compared to other knownbehaviors and application phenotypes, the system must be able to monitorand profile code execution in the pertinent portions of the operatingsystem. Thus, turning now to FIG. 1, an illustration of an exampleembodiment of a system 100 for profiling code execution is shown. System100 may be configured to profile the execution of code on an electronicdevice as it is dynamically loaded and executed. By profiling theexecution of code, system 100 may monitor the execution for patterns ofexecution that indicate malware. In one embodiment, system 100 mayperform such monitoring without the use of an operating system orapplication hooking. In another embodiment, system 100 may perform suchmonitoring through the use of exception loading on a processor. In yetanother embodiment, system 100 may perform profiling of code executionby recording and evaluating transitions between different addressspaces.

System 100 may include an anti-malware module 110 configured to evaluatewhether an electronic device 102 is infected with malware. Anti-malwaremodule 110 may be resident upon electronic device 102 or upon anelectronic device communicatively coupled to electronic device 102.Anti-malware module 110 may be communicatively coupled to a processor106 of electronic device 102 and to malware-microstep rule logic 108.Anti-malware module 110 may be configured to access malware-microsteprule logic 108 to determine what portions of a memory 104 of electronicdevice to monitor. Furthermore, anti-malware module 110 may beconfigured to access malware-microstep rule logic 108 to determine whattransitions from a given portion of memory to another to monitor.Anti-malware module 110 may be configured to define such rules intoprocessor 106 by, for example, configuring processor 106 to generateexceptions when a transition occurs between a first defined addressspace (or range) to a second defined address space (or range) in aparticular way. Processor 106 may be configured to send a resultingexception to anti-malware module 110. Anti-malware module 110 may beconfigured to access malware-microstep rule logic 108 to determinewhether a transition detected by processor 106—along with previouslydetermined transitions—fulfills a sequence, microstep, behavior,phenotype or other rule that is associated with malware.

Malware-microstep rule logic 108 may be resident upon electronic device102, or upon any other electronic device that is accessible byanti-malware module 110. In one embodiment, malware-microstep rule logic108 may be resident upon an anti-malware server on a networkcommunicatively coupled to anti-malware module 110. In anotherembodiment, malware-microstep rule logic 108 may be loaded in memory104, processor 106, or anti-malware module 110. Malware-microstep rulelogic 108 may be implemented in any suitable manner, such as with aserver, proxy, application, module, script, library, function, logic,database, file, table, or other data structure or entity.Malware-microstep rule logic 108 may include any suitable informationfor defining regions of memory 104 to monitor, transitions to monitor,I/O ports to monitor, memory areas for reading or writing, or sequencesof transitions (e.g., microsteps) that indicate malware.

Malware-microstep rule logic 108 may include positive rules, by which amonitored operation or sequence (e.g., a first-tier microstep) or aparticular sequence of sequences (e.g., a second-tier microstep) isrecognized as safe or “normal,” or it may employ negative rules, bywhich a monitored operation, sequence, or sequence of sequences isrecognized as malicious. Furthermore, malware may manipulate an expectedorder of execution in legitimate software, such that individualoperations are not malicious and are in fact related to legitimatesoftware, but are conducted in a sequence such that the order of suchoperations is malicious. Such malware may include, for example,return-oriented programming (ROP). By use of positive rules incombination with one or more negative rules, in comparison to known“normal” application behaviors, one or more “false positive”identifications of malware may be eliminated.

Electronic device 102 may be implemented in any suitable manner. Forexample, electronic device 102 may include a mobile device, computer,server, laptop, desktop, board, or blade.

Anti-malware module 110 may be implemented in any suitable manner. Forexample, anti-malware module 110 may include instructions, logic,functions, libraries, shared libraries, applications, scripts, programs,executables, objects, analog circuitry, digital circuitry, or anysuitable combination thereof.

Processor 106 may comprise, for example, a microprocessor,microcontroller, digital signal processor (DSP), application specificintegrated circuit (ASIC), or any other digital or analog circuitryconfigured to interpret and/or execute program instructions and/orprocess data. In some embodiments, processor 106 may interpret and/orexecute program instructions and/or process data stored in memory 104.Memory 104 may be configured in part or whole as application memory,system memory, or both. Memory 104 may include any system, device, orapparatus configured to hold and/or house one or more memory modules.Each memory module may include any system, device or apparatusconfigured to retain program instructions and/or data for a period oftime (e.g., computer-readable storage media). Instructions, logic, ordata for configuring the operation of system 100, such as configurationsof components such as electronic device 102 or anti-malware module 110may reside in memory 104 for execution by processor 106.

Processor 106 may execute one or more code instruction(s) to be executedby the one or more cores of the processor. The processor cores mayfollow a program sequence of instructions indicated by the codeinstructions. Each code instruction may be processed by one or moredecoders of the processor. The decoder may generate as its output amicro operation such as a fixed-width micro operation in a predefinedformat, or may generate other instructions, microinstructions, orcontrol signals which reflect the original code instruction. Processor106 may also include register renaming logic and scheduling logic, whichgenerally allocate resources and queue the operation corresponding toconverted instructions for execution. After completion of execution ofthe operations specified by the code instructions, back end logic withinprocessor 106 may retire the instruction. In one embodiment, processor106 may allow out of order execution but requires in order retirement ofinstructions. Retirement logic within processor 106 may take a varietyof forms as known to those of skill in the art (e.g., re-order buffersor the like). The processor cores of processor 106 are thus transformedduring execution of the code, at least in terms of the output generatedby the decoder, the hardware registers and tables utilized by theregister renaming logic, and any registers modified by the executionlogic.

Anti-malware module 110 may be configured to define any suitable portionof memory 104 for monitoring. Anti-malware module 110 may make suchdefinitions through referencing, for example, malware-microstep rulelogic 108. For example, anti-malware module 110 may define the portionsof memory 104 used for kernel mode operation. In the example of FIG. 1,anti-malware module 110 may define that kernel space, including thememory addresses from (0x000) to ((M*F) x FFF) is to be monitored as aregion. In another example, anti-malware module 110 may define theportions of memory 104 used for user mode operation is to be monitoredas a region. In the example of FIG. 1, anti-malware module 110 maydefine that user space, including the memory addresses from ((M*F) xFFF) to ((N*F) x FFF), is to be monitored as a region.

Specific applications, processes, or threads may be identified formonitoring by anti-malware module 110. These may be dynamically trackedas they are loaded into memory 104. In the example of FIG. 1,anti-malware module 110 may define that a particular tracked process, asloaded in the region denoted by R5, is to be monitored. The trackedprocess may represent a process for which it is unknown whether theprocess is associated with malware.

Specific portions of an operating system may be identified formonitoring by anti-malware module 110. These portions may be dynamicallytracked as they are loaded into memory 104. Memory for any suitableportion of an operating system may be monitored. In the example of FIG.1, anti-malware module 110 may define that a System Service DispatchTable (SSDT) as loaded in the region denoted by R1 and a user-portion“X” of an operating system as loaded in the region denoted by R7 may bemonitored. Specific elements within regions or operating systemconstructs, such individual pointers in an SSDT, may also be monitored.Such individual pointers may indicate access of specific, identifiedfunctions managed by the SSDT.

Other entities that may be monitored as they reside in memory 104 mayinclude, for example, system shared libraries—including dynamicallylinked libraries (DLLs), interrupt descriptor tables (IDT), system calltables, user shared libraries, or operating system call dispatchers.Such entities may be used in any suitable operating system. Otherregions of memory 104 may be defined, such as regions R2, R3, R4 ofkernel space and region R6 of user space.

Furthermore, anti-malware module 110 may be configured to identifyparticular microsteps, e.g., aggregations of individual low-leveltransitions, reads, writes, executions, attribute changes, or I/O portattribute changes to be used for profiling code executions. Theparticular aggregations of transitions and accesses may be defined inmalware-microstep rule logic 108. Example transitions are illustrated inFIG. 1 and may include: a transition 114 from a known portion of userspace such as the user-portion “X” of the operating system in R7 to thetracked process in R5; a transition 124 from unidentified entities inkernel space in R4 to the tracked process in R5; a transition 122 fromone unidentified entity in kernel space in R4 to another unidentifiedentity in kernel space in R3; a transition 118 from the tracked processin R4 to a known entity in kernel space such as SSDT in R1; a transition116 from the tracked process in R4 to an unidentified entity in kernelspace in R3; or a transition 120 from an unknown entity in kernel spacein R2 to a known entity in kernel space such as SSDT in R1.

In addition, anti-malware module 110 may be configured to identifyparticular aspects of transitions for the purposes of profiling codeexecutions. The particular aspects may be defined in malware-access rulelogic 108. In one embodiment, the specific direction of a transition maybe evaluated. For example, a transition between R1 to R2 may bedistinguished from a transition between R2 to R1. Thus,malware-microstep rule logic 108 may require that a given transition inan aggregation of transitions that is defined a microstep is to bematched only in a specified direction. In another embodiment, thedistance of a transition may be evaluated. For example, a transitionfrom one memory space to a nearby memory space may be sufficiently shortso as to represent a transition that is to be ignored. Thus,malware-microstep rule logic 108 may provide an exception for atransition if the transition is made across less than a threshold amountof memory addresses. Such a threshold may include, for example, fiftybytes. In yet another embodiment, a count of a transition may beevaluated. For example, a transition from one monitored region toanother may be repeated multiple times, and only upon a certain numberof such repeated transitions will the execution be deemed worthy ofapplying malware-microstep rule logic 108. Thus, malware-microstep rulelogic 108 may specify that a transition is to be matched to a knownmicrostep only upon an n-th time such a transition occurs. Previous orsubsequent instances of such transitions may be ignored. In still yetanother embodiment, conditionals may be placed upon transitions, suchthat logic, including NOT, AND, OR, XOR, or other such operations may beapplied to one or more transitions. For example, a particular transitionmay indicate the presence of a particular microstep, as long as another,different kind of transition has not yet been encountered. Thus,malware-microstep rule logic 108 may specify conjugate or complexlogical conditions for matching transitions.

Transitions such as transitions 114, 116, 118, 120, 122, and 124 may bedefined as branches in execution between different regions of memory104. In one embodiment, a jump in execution may be evaluated accordingto addresses loaded into an instruction pointer 112 of processor 106.Memory addresses of a given range loaded into instruction pointer 112before execution may be compared to memory addresses of another givenrange loaded into instruction pointer 112 after execution. The change inmemory ranges associated with instruction pointer 112 before and afterexecution may reflect a JMP instruction, a CALL instruction, or otherinstruction for branching execution between entities resident in memory104. In another embodiment, reads, writes, or executions of memoryaddresses may define branches of execution, wherein subsequent reads,writes, or executions of memory that are in disparate portions of memory104 may be considered transitions. In yet another embodiment,instruction fetching may be used to determine a point of execution inmemory 104.

Timing and instruction counts may also be taken into account inmalware-microstep rule logic 108. For example, if a time interval—suchas that determining by a central processing unit counter—between twotransitions is too long or too short then a given rule may not apply.Similarly, if an instruction counter is below or above a threshold thenthe state of matching a rule may be reset to an initial state.

While a single processor 106 is illustrated in FIG. 1, system mayinclude multiple processors. Furthermore, processor 106 may includemultiple cores or central processing units. Profile execution performedby system 100 may be conducted on a per-processor, per-core, orper-central processing unit basis. Furthermore, such profile executionmay be conducted on a per-process or per-thread basis on any suchprocessor, core, or central processing unit.

The illustration of code execution profiling with transitions in FIG. 1is made with reference to contiguous memory. Such profiling may includeprofiling of entities as such entities are resident within virtualmemory. In one embodiment, the profiling of code execution may beaccomplished by reference to physical addresses underlying the virtualmemory.

As described above, anti-malware module 110 may access malware-microsteprule logic 108 to determine any suitable number or kinds of transitions(or sequences of transitions) that are to be monitored. In oneembodiment, rules, indications, or logic for such transitions that areto be monitored may be loaded into, for example, processor 106 or into arandom-access-memory controller coupled to processor 106. Uponencountering such a transition defined by the rules, processor 106 maygenerate a page fault, interrupt, or an exception that may be handled byanti-malware module 110. In another embodiment, rules, indications, orlogic for such transitions that are to be monitored may be loaded into abelow-operating system trapping agent, such as a hypervisor or virtualmachine monitor. Upon encountering such a transition (or sequences oftransitions) defined by the rules, the hypervisor or virtual machinemonitor may generate a virtual machine exit or other notification thatmay be handled by anti-malware module 110. Anti-malware module 110 mayfurther access malware-microstep rule logic 108 to determine whether thedetected transition (or sequences of transitions) are indicative ofmalware. During handling of the notification, execution may be paused.

The notification made by processor 106 that a rule has been matched mayinclude context information. The information may include anidentification of the rule that was matched, the memory locationaccessed, the memory location originating the access, and any associatedparameters such as values resident on the execution stack of processor106.

Anti-malware module 110 may be configured to track the occurrences ofmultiple detected transitions to determine whether such transitionsinclude an operational pattern indicative of malware. Such patterns maybe defined in, for example, malware-microstep rule logic 108. If the oneor more detected transitions are not indicative of malware, anti-malwaremodule 110 may be configured to allow the transition. If the one or moredetected transitions may be indicative of malware pending additionaldetermined transitions, anti-malware module 110 may be configured toallow the transition but record the transition for possible futurecorrective action.

Upon determining that one or more transitions are indicative of malware,anti-malware module 110 may be configured to take any suitablecorrective action. For example, the entities in memory 104 associatedwith the transitions indicating malware may be quarantined, reported,removed, cleaned, or otherwise handled. Furthermore, anything associatedwith such entities, such as contents in storage or on disk, or otherexecuting processes in memory may be quarantined, reported, removed,cleaned, or otherwise handled.

Certain interrupts or notifications may be defined according to awhitelist of malware-microstep rule logic 108. Placement of suchinterrupts or notifications into the whitelist may cause anti-malwaremodule 110 to ignore such interrupts in terms of malware analysis. Suchinterrupts or notifications may be the result of, for example, known orsafe elements of system 100 or may be unrelated to malware and thusrequire no handling by anti-malware module 110.

Turning now to FIG. 2, a block diagram is shown, illustrating exemplarymalware microstep rules and application phenotype logic 108, accordingto one embodiment. Malware-microstep rule logic 108 may includemonitored regions 202. Monitored regions 202 may define entities thatare to be monitored. In one embodiment, monitored regions 202 may definememory ranges of memory 104 for which execution will be profiled bysystem 100. The memory ranges may be defined according to physicalmemory addresses or virtual memory addresses, as appropriate. Themapping of the memory ranges corresponding to a given entity may bedefined according to, for example, anti-malware module 110 as it tracksthe loading and unloading of elements of memory 104. In the example ofFIG. 2, monitored regions 202 may define that user space and kernelspace are to be monitored. Such monitored regions 202 may define thatuser space may be resident from addresses (M*F x FFF) through (N*F xFFF), and that kernel space may be resident from addresses (0x000)through (M*F x FFF). Specifically, elements may be defined in monitoredregions 202, such as SSDT, a tracked process, and a user-portion “X” ofan operating system. Although specific memory address ranges are notdetailed in the example of FIG. 2, such addresses may be specified afterthey are determined. Furthermore, each such region may be given a uniqueidentifier or otherwise labeled for reference by other rules. Kernelspace and user space may be referenced as such. The space for SSDT maybe denoted as R1, the space for the tracked process may be denoted asR5, and the space for the user-portion “X” of the operating system maybe denoted as R7.

Malware-microstep rule logic 108 may include operations and API callsmonitored 204, which may specify what transitions or other operationsmay require handling for malware evaluation. For reference by other ofportions of malware-microstep rule logic 108, each of operationsmonitored 204 may be denoted by a unique identifier. The uniqueidentifier may thus represent a state of operation in which themonitored operation has occurred. For example, a call, jump, or otherbranch from R5 (the tracked process) into R7 (the user portion “X”) maybe considered a transition and marked as state (A). A call, jump, orother branch from R7 (the user portion “X”) to anywhere within thekernel space may be considered a transition and be marked as state (B).A call, jump, or other branch from anywhere in the kernel space to R1(the SSDT) may be considered a transition and be marked as state (C). Acall, jump, or other branch anywhere in the kernel space to anywhereelse in the kernel space may be considered a transition and be marked asstate (D). Such an event may be subject to a minimum distancerequirement. A call, jump, or other branch anywhere in the kernel spaceto anywhere else in the user space may be considered a transition and bemarked as state (E). The monitored operations may also include contextfrom higher-level APIs, such as those to open file dialogs, createcookies, or perform a DNS lookup. This can be helpful because, e.g., itis easier to aggregate disparate operations into a single microstep ifit is known that the application called an API to trigger theperformance of the operations in question.

The specific operation which is monitored within each of operationsmonitored 204 may include any suitable operation. The operation may bespecific to the type of entity. For example, such monitoring may includeexecution of certain entities in R5 or merely accessing data in theentities in R1.

Malware-microstep rule logic 108 may include First-tier microstep rules206, which may specify how to classify a given sequence of encounteredoperations, such as those defined in operations monitored 204. Each ofFirst-tier microstep rules 206 may be represented by, for example, anaggregation of low-level operations and higher-level contextualinformation delivered by APIs. For example, given detected operations(A), (D), (E), and (B), First-tier microstep rules 206 may indicate thatsuch a detected sequence is representative of “Create Internet Cookie”microstep (1^(st) MS-1). In one embodiment, the microstep rule logic mayrequire the no other detected ones of operations monitored 204 to haveappeared in addition to operations (A), (D), (E), and (B) in order forthe operations to be classified as a “Create Internet Cookie” First-tiermicrostep. In another embodiment, the microstep may allow a certainamount of deviation or “tolerance” in the detection of operations thatwould be classified as a “Create Internet Cookie” First-tier microstep.In still other embodiment, the microstep may also take intoconsideration whether the sequence of operations were executed inresponse to an API call made to a particular program or library. Theremaining First-tier microsteps (1^(st)MS-2)-(1^(st) MS-5) representadditional hypothetical First-tier microsteps that the system may beconfigured to recognize in response to a give sequence of encounteredoperations.

First-tier microstep rules 206 may thus specify behaviors of a generaltype, without relying upon signature identification of a particularentity as malware. Behaviors of polymorphic malware, which may changeits own content (and consequently, signature) without its overallfunctionality, may thus be detected. Furthermore, unusual or unexpectedpatterns of operation may be detected. Such unusual or unexpectedpatterns of operation may reflect a new, unknown kind of malware. Forexample, a process may match a given prefix of a sequence of operationthat otherwise may indicate operation of a trusted process, but thenperform an atypical access for such a trusted process. In such cases,the process may not match any of the First-tier microstep rules 206,indicating that the sequence of operations may be indicative of malware.

Malware-microstep rule logic 108 may further include Second-tiermicrostep rules 208, which may specify how to classify a given sequenceof encountered First-tier microsteps, such as those defined inFirst-tier microstep rules 206. Each of the Second-tier microstep rules208 may be represented by, for example, an aggregation of First-tiermicrostep rules 206. For example, given detected First-tier microsteps(1^(st)MS-2) and (1^(st)MS-4) [and potentially others First-tiermicrosteps, if so desired], Malware-microstep rule logic 108 may be ableto identify a Second-tier microstep, (2^(nd)MS-1), indicative of an“Open File Dialog Box” operation. As mentioned above, Second-tiermicrosteps may be used to further “up-level” more complex applicationbehaviors and provide further tolerance in the underlying operations,while still being able to identify a sequence of underlying operationsas a particular higher-level concept.

Malware-microstep rule logic 108 may still further include Behaviorrules 210, which may specify how to classify a given sequence ofencountered Second-tier microsteps, such as those defined in Second-tiermicrostep rules 208. Each of the Behavior rules 210 may be representedby, for example, an aggregation of Second-tier microstep rules 208. Forexample, given detected Second-tier microsteps (2^(nd)MS-1) and(2^(nd)MS-2) [and potentially others Second-tier microsteps, if sodesired], Malware-microstep rule logic 108 may be able to identify aBehavior, (B-2), indicative of a “User Requesting a File to be Opened”behavior. Behaviors may be used to further “up-level” the combination ofcomplex Second-Tier microsteps into common “use cases” or activitiesperformed by an application operating under “normal” conditions (and toprovide further tolerance in allowing for variations to the underlyingoperations, while still being able to identify a sequence of underlyingoperations as a particular higher-level behavior).

Finally, malware-microstep rule logic 108 may further includeApplication Phenotypes 212, which, as described above, may detail a listof “known” or “normal” behaviors that may be performed by a givenapplication. As described above, each of the Behavior rules 210 may berepresented by an aggregation of Second-tier microsteps 208, which may,in turn, be comprised of First-tier microsteps 206, which may, in turn,be comprised of individual operations and API calls detected bymonitoring particular memory regions and transitions. As may now be morefully appreciated, the use of Application Phenotypes provides fortolerance at every level of detection (i.e., operation, First-tiermicrostep, Second-tier microstep, Behavior), which can help to limit thenumber of false positives identified by the malware system and providethe system with further robustness in making the determination of“normal” versus “abnormal” behavior, without reference to the actualcontent or signature or any particular process. For example, ApplicationPhenotype (AP-1) may refer to the “Explorer.exe” application, and maydefine behaviors B-1, B-2 and B-5 as being normal behaviors that the“Explorer.exe” application exhibits “in the wild.” Thus, when anabnormal behavior is detected for a given application (i.e., a behaviorthat is not part of that application's phenotype), the system may flagthe behavior for further intervention, or stop the behavior fromoccurring outright.

FIG. 3 is a diagram 300 illustrating the relationships between exemplaryapplications, application phenotypes, and individual behaviors,according to one embodiment. Applications Explorer.exe 310 andNotepad.exe 315 are represented by the two larger rings. Within ring 310for Explorer.exe are Behaviors #1, #2, and #5, indicating that thesebehaviors are a part of the application phenotype for Explorer.exe.Likewise, within ring 315 for Notepad.exe are Behaviors #3 and #4,indicating that these behaviors are a part of the application phenotypefor Notepad.exe. Further, the individual behaviors 320/322/324/326/328are shown as being comprised of various Second-tier microsteps. Whilenot shown as such, it is possible for multiple applications to share thesame behaviors as one another. Recall that, as stated earlier, eachSecond-tier microstep may itself be comprised of one or more First-tiermicrosteps, which are in turn recognized by the system monitoring aparticular aggregation or sequence of lower-level operations in thesystem.

FIG. 4 is a block diagram 400 illustrating a computer-implemented systemfor building and maintaining application phenotypes, according toanother embodiment. First, security cloud 410 represents theaforementioned central device for aggregating and parsing the monitoredevent data from the end points monitored by the malware detectionsystem. Security cloud 410 may comprise a “Big Data” engine 422, whichin turn may comprise an event store 426 for storing all of the monitoredevents and microsteps observed at the monitored end points and eventprocessor 424 for attempting to parse and glean useful information fromthe captured event data, e.g., the determination of existingmicrosteps/behaviors/phenotypes and/or the discovery of newmicrosteps/behaviors/phenotypes in the collected data. Once the Big Dataengine 422 has determined or discovered newmicrosteps/behaviors/phenotypes, they may be stored in microsteppatterns database 428 and pushed/pulled to and from end points via APIedge 430.

End point device 440 represents an end user's computer, e.g., a desktopcomputer, mobile device, tablet device, etc. that is utilizing themalware protection system. As mentioned above, in some embodiments,monitoring may be performed by a home gateway device 402, rather thanall processing being done by a central security cloud 420 or a clientapplication running on the end point device itself. Thus, in addition toperforming packet processing 404 to and from the Internet 418, homegateway 402 may also comprise a gateway event processor 406 forprocessing the various events (i.e., operations) monitored by thedevice. Gateway event processor 406 may comprise an event trimmer 408for parsing out irrelevant or unimportant collected event data. The datafrom the event trimmer 408 may then be sent to the microstep finder 410,e.g., employing the malware-microstep rule logic 108 to see if any knownmicrosteps may be located in the event data. Any located microsteps maythen be passed to the microstep analyzer 412 to determine if any knownbehaviors may be determined. If the sequence of microsteps determineddoes not relate to a known behavior, they may eventually be uploaded byunknown event uploader 416 to the security cloud 420 via the API edge430. If, instead, the microstep analyzer recognizes the determinedmicrosteps as an abnormal behavior, a security policy may be enforced bypolicy enforcer 414. Policy enforcer module 414 may receive one or moreidentified behaviors from the event processor 406 and determine whethereach of the one or more identified behaviors are abnormal, e.g.,indicative of malware, based, at least in part, on comparisons to: aphenotype of the client application running on the end point device thatcaused the respective identified behavior to occur, a phenotype for oneor more other trusted applications (e.g., one that is not running on theend point device); and a phenotype for one or more known malwareapplications. For example, in some embodiments, the policy enforcermodule may be configured to determine that an identified behavior isindicative of malware when the phenotype of the application that causedthe identified behavior to occur does not comprise the identifiedbehavior, and the phenotype of the one or more trusted applications doesnot comprise the identified behavior. In other embodiments, the policyenforcer module may also be configured to determine that an identifiedbehavior is indicative of malware when the phenotype of one or moreknown malware applications comprises the identified behavior.

As mentioned above, system 400 may also comprise individual Windowsclient devices 450 that are running their own application phenotypingmalware detection software. Client 450 may thus comprise an eventcollector 466 in communication with ETW 468, and with context hooks472/476 into various applications, such as Acrobat Reader 470 or Browser474. The various events and operations collected by event collector 466may then be passed to the Windows Client Event Processor 454. The makeupof Windows Client Event Processor 454 may be similar to that of GatewayEvent Processor 406 described above (i.e., comprising Event Trimmer 460,Microstep Finder 462, Microstep Analyzer 456, Unknown Event Storage 458,and Unknown Event Uploader 464). However, in addition to uploadingunknown event data to the Security Cloud 420 with the Unknown EventUploader 466, the Windows Client Event Processor 454 may also store theunknown event data in Unknown Event Storage 458, which may comprise aSecure Storage device 480.

The architecture of system 400 highlights several key aspects of certainembodiments of the application phenotyping system. First, a centralized,cloud-based storage and processing may be used for microstep generation(APIs exist to deliver microsteps to user end points in the appropriatedata formats). Second, for closed operating systems where the centraldevice has no access, microsteps may be built out based on an individualnetwork (these systems could also include industrial computers that arepart of the so-called Internet of Things (IOTs), hence the home gateway402 is shown as potentially doing some microstep analysis. Third, in theclassic home end-user Windows client example, where there is typicallyquite heavy data collection on all of the different kernel access, APIusage, etc., fronts, the microstep analysis may be performed “on-box”(i.e., on the Windows device), as opposed to anywhere else. In largersystems with, e.g., those with millions of events, sending all of theevents to a central device for analysis will not scale well. To relievesome of this stress, the clients on user end points could instead uploadtheir microstep data at predetermined time intervals, e.g., every 8hours. As microsteps are discovered “in the wild” by a given end pointin the event streams from all the monitored applications, there may endup being a large number of “unknown” microsteps. These unknownmicrosteps could either by an indication of something malicious, or theycould simply indicate that, in the wild, there is software that is doingcompletely benign—but very ‘different’—behavior. When this informationis reported back to the central device, it may be an indication that anew microstep(s) should be built. Periodically, end points could uploadall its microstep detection data, along with metrics, to indicateusefulness of this approach and how the device is performing. In anideal implementation the microstep templates and other information isdownloaded over a secured connection, so that it may be ensured that theend points are operating on a correct data set.

FIG. 5 is a block diagram illustrating a computer system 500 that may beused to implement some or all of the techniques described herein. Asystem unit 510 provides a location where components of the computersystem 500 may be mounted or otherwise disposed. The system unit 510 maybe manufactured as a motherboard on which various chipsets are mounted,providing electrical connection between the components and signal andpower distribution throughout the system unit 510 and external to thesystem unit 510 as desired. For example, the computer system 500 mayinclude an output device such as display 595, which provides a way todisplay alerts or other indications that the anti-malware system hasdetected the possibility of an anomaly by examining hardened platformcounters.

Various components of the system unit 510 may include one or moreprocessor 520, typically each a single processor chip mounted in amounting socket (not shown in FIG. 5) to provide electrical connectivitybetween the processors 520 and other components of the computer 500.Although a single processor 520 is illustrated in FIG. 5, any desirednumber of processors can be used, each of which may be a multi-coreprocessor. Multiple processor chips are available on the marketcurrently, and any desired processor chip or chipset may be used. Thesystem unit 510 may be programmed to perform methods in accordance withthis disclosure, an example of which is illustrated in FIGS. 6 and 7.

The processor 520 is connected to memory 530 for use by the processor520, typically using a link for signal transport that may be a bus orany other type of interconnect, including point-to-point interconnects.Memory 530 may include one or more memory modules and comprise randomaccess memory (RAM), read only memory (ROM), programmable read onlymemory (PROM), programmable read-write memory, and solid-state memory.The processor 520 may also include internal memory, such as cachememory. An operating system running on the processor 520 generallycontrols the operation of the computer system 500, providing anoperating system environment for services, applications, and othersoftware to execute on the computer 500.

As illustrated in FIG. 5, processor 520 is also connected to a I/Osubsystem 540 that provides I/O, timer, and other useful capabilitiesfor the computer system 500. For example, the I/O subsystem 540 mayprovide I/O ports for connecting an optional display 595 and an optionalinput device 590, such as a keyboard, mouse, touch screen, to the systemunit 510. The ports may be either one or more of special-purpose portsfor components like the display 595 or multipurpose ports such asUniversal Serial Bus (USB) ports for connecting a keyboard or mouse 590.The I/O subsystem 540 may also an interface for communicating withstorage devices such as storage device 580, connect to audio devicesthrough an audio interface 560, and connect to a network via networkinterface 570. The storage device 580 represents any form ofnon-volatile storage including, but not limited to, all forms of opticaland magnetic, including solid-state storage elements, includingremovable media, and may be included within system unit 510 or beexternal to system unit 510. Storage device 580 may be a program storagedevice used for storage of software to control computer 500, data foruse by the computer 500 (including network flow data), or both. Althoughonly a single storage device 580 is illustrated in FIG. 5 for clarity,any number of storage devices 580 may be provided as desired, dependingon interface availability. The I/O subsystem 540 may be implemented asone or more chips within the system unit 510. In some embodiments, thememory 530 may be connected to the I/O subsystem 540 instead of to theprocessor 520.

In addition, some embodiments may connect the I/O subsystem 540 to aTrusted Platform Module 550 that provides a cryptoprocessor for storingcryptographic keys to protect information. Embodiments may implement thefunctionality of the I/O subsystem 540 as one or more separate chips inthe system unit 510.

As illustrated in FIG. 5, the I/O subsystem 540 provides hardwareresources for the secure trusted environment (TE) 545. The TE 545provides a secure environment not controlled by the operating systemthat controls the computer 500. In other embodiments, the TE 545 may beoutboard of the I/O subsystem as a separate chipset, or may beincorporated in the processor 520, such as a separate core restricted toTE functionality. The TE 545 contains secure processing functionalitythat allows performing the secure environment side in a trustedenvironment that cannot be interfered with by malware—even malware thatmay run as a bootkit or rootkit on processor 520. Typically, vendorsproviding the TE 545 use proprietary or cryptographic techniques toensure control over what functionality may execute in the TE 545,preventing execution of any but carefully vetted trusted programs to runin the TE 545. Special interfaces may be provided to allow softwarerunning on the processor 520 to request the TE 545 to perform desiredfunctionality or providing data from the TE 545 to the processor 520 foranalysis. The TE 545 may either use its own internal memory or use aportion of the memory 530 for data and firmware storage. Alternatively,instructions in the form of firmware for execution in the TE 545 may beloaded from a non-volatile memory device 545, such as a flash memory,upon powering up of the computer 500, and then loaded into a portion ofthe memory 530 for execution by the TE 545. In some embodiments, the TE545 may be disabled and enabled as desired. These instructions may causethe TE 545 to perform the desired functionality. An example of a trustedenvironment that may be used for these techniques is the ManageabilityEngine (ME) in certain chipsets provided by INTEL® Corp. Althoughdescribed herein generally in terms of a hardware-based TE 545, secureenvironments, e.g., the Intel® SGX type of TE, may be implemented inhardware, firmware, or software, or any combination thereof, as desired.

The computer system 500 may be any type of computing device, such as,for example, a smart phone, smart tablet, personal digital assistant(PDA), mobile Internet device (MID), convertible tablet, notebookcomputer, desktop computer, server, or smart television. The display595, if present, may be any time of device for presenting an interfaceto the user, such as, for example, a touch screen or a liquid crystaldisplay. The elements illustrated in FIG. 5 are illustrative and givenby way of example only. The elements shown in FIG. 5 may be combined ordivided into multiple elements as desired. Other elements, such asgeo-positioning logic provided by a Global Positioning System (GPS)transceiver, as well as logic for handling mobile communications usingstandards such as, for example, IEEE 802.11, IEEE 802.16, WiMax, etc.,may also be provided as desired.

FIG. 6 is a flowchart illustrating a technique 600 for buildingapplication phenotypes, according to one embodiment. At Step 610,entities of an electronic device to be monitored may be determined. Suchentities may include, for example, kernel space, user space, an SSDT, anoperating system call dispatcher, system DLLs, user DLLs, processes, I/Oports, heaps, threads, or a system stack. At Step 620, regions of memoryassociated with such entities may be determined as such entities resideon the electronic device. At Step 630, operations upon the regions ofmemory and entities may be determined to be monitored. Such operationsmay include, for example: jumps, calls, or other branches of execution.The operations may be defined directionally, from one memory region toanother. At Step 640, patterns of the operations to be monitored may bedetermined. Such patterns may specify particular orders of theoperations through commands, logic, or state machines. The patterns mayrepresent, for example, one or more First-tier microsteps. At Step 650,particular patterns or sequences of First-tier microsteps may berecognized and determined to be one or more Second-tier microsteps. AtStep 660, particular patterns or sequences of Second-tier microsteps maybe recognized and determined to be one or more behaviors. Finally, atStep 670, one or more behaviors may be associated with the “normal”operations of an application, i.e., an application's phenotype.

The determinations made by method 600 (e.g., as to what entities tomonitor, what patterns represent microsteps, what microsteps representbehaviors, etc.) may be performed by, for example: anti-malwareresearch; profiling a specific electronic device or entities thereof;profiling a class of electronic device or entities thereof; accessing ananti-malware server; accessing anti-malware rule logic; readinganti-malware definition files; or through any other suitable process.The result of method 600 may be the availability of one or more“application phenotype” rules by which code execution may be profiled.

FIG. 7 is a flowchart illustrating a method 700 for detecting maliciousbehavior, according to one embodiment. As discussed above with referenceto FIG. 4, the method 700 may be performed by any number of centraldevices, e.g., a home gateway device, a Windows (or other OS) clientdevice, and/or in the cloud by a network-accessible server. At Step 710,the central device that will be performing the application phenotypeprocessing may begin by loading the “normal” behaviors associated with aparticular application (e.g., those behaviors identified by method 600of FIG. 6) into a processor memory space. At Step 720, the centraldevice may begin to monitor the live operations taking place on themonitored device (e.g., using the techniques outlined with reference toFIG. 2 and FIG. 6). At Step 730, the central device may combine all themonitored operations into tiered aggregations of microsteps andbehaviors in order to determine a phenotype for the application. At Step740, the central device may store the application phenotype, either toanalyze ‘on site’ or to upload to a ‘security cloud,’ e.g., securitycloud 420 with big data engine 422, for further analysis. At Step 750,it may be determined by the central device whether the determinedapplication phenotype from Step 730 contains all normal behaviors forthe respective application. If all behaviors monitored are “normal”(within a threshold level, as will be discussed below), the process mayreturn to Step 720 to continue monitoring the execution of operations onthe monitored device(s). If, instead, all the behaviors monitored arenot “normal” (within a threshold level, as will be discussed below), theprocess may proceed to Step 760 to determine, by the central device,whether the determined application phenotype from Step 730 contains allnormal behaviors for the another ‘trusted’ application (i.e., anapplication that is known to not be malware). If all behaviors monitoredare “normal” (within a threshold level, as will be discussed below) forsome other ‘trusted’ application, the process may likewise return toStep 720 to continue monitoring the execution of operations on themonitored device(s). If, instead, there are monitored behaviorsmonitored that are not “normal” to the application (or any other trustedapplication), or, indeed, if there are monitored behaviors thataffirmatively match (within a threshold level) the behaviors of a knownmalware application, the process may proceed to Step 770 to indicate apossible malware process has been identified and then optionally act onthe detected malware process at Step 780. In some embodiments, theprocess may determine that a particular behavior is indicative ofmalware by determining a confidence score based on the comparisons ofthe particular behavior to each of: the phenotype for the particularapplication, the phenotype for one or more trusted applications, and thephenotype for the one or more known malware applications. By combiningthese comparisons, e.g., using a weighting scheme, a single confidencescore may be determined that is used for the ultimate determination ofwhether or not the particular behavior is indicative of a malwareprocess present on the monitored device. E.g., in some embodiments,whether a behavior matches with the behavior of a known malwareapplication may be given more weight (and thus ultimately cause theconfidence score to indicate a possible malware process) than adetermination that a behavior matches a behavior of another trustedapplication. In still other embodiments, whether a behavior matches withthe normal behavior of the particular application may be given moreweight (and thus ultimately determine whether the confidence scoreindicates a malware process) than a determination that a behaviormatches a behavior of some other trusted application.

With respect to the determinations made at Step 750 and Step 760 withregard to whether or not an observed application phenotype contains all“normal” behaviors, method 700 may employ one or more scoring techniquesto develop a reputation and/or confidence score that an observedbehavior matches a known, normal behavior for a given application. Forthe scoring process, the method may consider traces from, e.g.: withinan entire enterprise; only on the device in question; or across alldevices globally, etc. The scoring process may compare the observedresults with one, many, or all of the above sets of devices to determinehow much the observed results deviate from known behaviors. For example,a scoring process may consider whether an observed behavior is adeviation from behaviors that have previously been observed on the samedevice and assign that a first weighting value, w1. It may then considerwhether an observed behavior is a deviation from behaviors that havepreviously been observed within the device's enterprise and assign thata second weighting value, w2. It may then consider whether an observedbehavior is a deviation from behaviors that have previously beenobserved across all devices globally and assign that a third weightingvalue, w3. All of these weights and scores may then be taken intoaccount when determining if an observed behavior is truly “abnormal.”For example, an exemplary combined score could be determined accordingto the following formula:(w1*DEVICE_SCORE)+(w2*ENTERPRISE_SCORE)+(w3*GLOBAL_SCORE). Each of theweights for these three classes of scores could be independently variedbased on a particular system, environment, or microstep, etc. If thescore (or combined score) for an exemplary process then differed from a“normal” behavior by a threshold amount, the process(es) may be deemed“abnormal.” and the method may proceed to Step 760 to indicate thepresence of potential malware.

For the purposes of this disclosure, computer-readable media may includeany instrumentality or aggregation of instrumentalities that may retaindata and/or instructions for a period of time. Computer-readable mediamay include, without limitation, storage media such as a direct accessstorage device (e.g., a hard disk drive or floppy disk), a sequentialaccess storage device (e.g., a tape disk drive), compact disk, CD-ROM,DVD, random access memory (RAM), read-only memory (ROM), electricallyerasable programmable read-only memory (EEPROM), and/or flash memory; aswell as communications media such wires, optical fibers, and otherelectromagnetic and/or optical carriers; and/or any combination of theforegoing.

As described above, malware may make changes to itself so as to disguiseits identity. Furthermore, malware authors may make changes to malwareto disguise itself. Changing the contents of malware may lead to achange in a digital hash or signature of the malware. Consequently,checking the identities of entities on the electronic device against alist of known malware signatures may not identify such changed malware.Code or execution analysis of static code may similarly be ineffective,as the code may change in such polymorphic malware.

An advantage of system 400 and methods 600 and 700 may include detectingsuch modified malware. Such detection may be performed by providingreliable detection of the underlying behaviors (and sequences ofbehaviors) being carried out by the malware. Furthermore, system 400 andmethods 600 and 700 may provide such dynamic profiling of execution thatis resilient to small or insignificant deviations in behaviors and/orsequences of behaviors. Thus, systems and methods described herein maybe able to detect the presence of malware—even if the malware isperforming an allowable operation for a given application—e.g., in theevent that the sequence of events leading up to the performing of theallowable operation was abnormal in some manner. Because the solutionsdescribed herein may also be considered content-less or “contentagnostic,” they do not need to ship to end users loaded with a number ofmalware-program specific information. Instead, they may build up aprofile of “normal” operations for machines running in a “clean”environment and then simply detect deviations from those operations.Such deviations may be harmless, or they may, in fact be, harmful; theimportant aspect is that they are simply flagged as “abnormal” in acontent-less fashion for further review.

The following examples pertain to further embodiments.

Example 1 is a malware detection system, comprising: a memory; amalware-microstep rule logic module, configured to: identify a pluralityof regions to be monitored on a first device; identify one or moreoperations between the regions to be monitored; identify one or moremicrosteps, each microstep comprising an aggregation or sequence ofoperations that represent a higher-level function; identify one or morebehaviors, each behavior comprising an aggregation or sequence ofmicrosteps that represent a normal activity performed by a firstapplication executing on the first device; identify a phenotype for thefirst application, the phenotype comprising each of the one or morebehaviors identified for the first application; and store the identifiedphenotype in the memory; a processor configured to, based upon themalware-microstep rule logic, generate a notification that the firstapplication has caused one or more of the operations to occur on thefirst device; and an anti-malware module configured, based on thenotification and the one or more operations that the first applicationcaused to occur, to: determine a first behavior performed by the firstapplication; compare the first behavior to the phenotype for the firstapplication; compare the first behavior to a phenotype for one or moretrusted applications, wherein the phenotype for a trusted applicationcomprises one or more behaviors identified for the respective trustedapplication, and wherein the one or more trusted applications aredifferent applications from the first application; compare the firstbehavior to a phenotype for one or more known malware applications,wherein the phenotype for a known malware application comprises one ormore behaviors identified for the respective known malware application,and wherein the one or more known malware applications are differentapplications from the first application; and determine whether the firstbehavior is indicative of malware based, at least in part, on thecomparisons of the first behavior to: the phenotype for the firstapplication, the phenotype for the one or more trusted applications, andthe phenotype for the one or more known malware applications.

Example 2 includes the subject matter of example 1, wherein thedetermination whether the first behavior is indicative of malwarecomprises determining a confidence score based on the comparisons of thefirst behavior to: the phenotype for the first application, thephenotype for the one or more trusted applications, and the phenotypefor the one or more known malware applications.

Example 3 includes the subject matter of example 1, wherein the malwaredetection system is located on the first device.

Example 4 includes the subject matter of example 1, wherein the malwaredetection system is communicatively coupled to the first device over anetwork.

Example 5 includes the subject matter of example 1, wherein thedetermination that a first behavior was performed by the firstapplication further comprises: comparing the one or more operations thatthe first application caused to occur with one or more known behaviorsusing a confidence score; and determining that the confidence scoreassociated with the comparison to the first behavior from among the oneor more known behaviors is above a threshold amount.

Example 6 includes the subject matter of example 5, wherein thedetermination that a first behavior was performed by the firstapplication further comprises comparing the one or more operations thatthe first application caused to occur with: one or more known behaviorsperformed on the first device; one or more known behaviors performed ondevices within the same enterprise as the first device; and one or moreknown behaviors performed on all devices monitored by the system.

Example 7 includes the subject matter of example 6, wherein theconfidence score is determined by independently weighting thecomparisons to the one or more known behaviors performed on: the firstdevice; devices within the same enterprise as the first device; and alldevices monitored by the system.

Example 8 is a method for performing malware detection, comprising:identifying a plurality of regions to be monitored on a first device;identifying one or more operations between the regions to be monitored;identifying one or more microsteps, each microstep comprising anaggregation or sequence of operations that represent a higher-levelfunction; identifying one or more behaviors, each behavior comprising anaggregation or sequence of microsteps that represent a normal activityperformed by a first application executing on the first device;identifying a phenotype for the first application, the phenotypecomprising each of the one or more behaviors identified for the firstapplication: storing the identified phenotype in a memory; generating anotification that the first application has caused one or more of theoperations to occur on the first device; determining, based on thenotification and the one or more operations that the first applicationcaused to occur, that a first behavior was performed by the firstapplication; comparing the first behavior to the phenotype for the firstapplication; comparing the first behavior to a phenotype for one or moretrusted applications, wherein the phenotype for a trusted applicationcomprises one or more behaviors identified for the respective trustedapplication, and wherein the one or more trusted applications aredifferent applications from the first application; comparing the firstbehavior to a phenotype for one or more known malware applications,wherein the phenotype for a known malware application comprises one ormore behaviors identified for the respective known malware application,and wherein the one or more known malware applications are differentapplications from the first application; and determining whether thefirst behavior is indicative of malware based, at least in part, on thecomparisons of the first behavior to: the phenotype for the firstapplication, the phenotype for the one or more trusted applications, andthe phenotype for the one or more known malware applications.

Example 9 includes the subject matter of example 8, wherein thedetermination whether the first behavior is indicative of malwarecomprises determining a confidence score based on the comparisons of thefirst behavior to: the phenotype for the first application, thephenotype for the one or more trusted applications, and the phenotypefor the one or more known malware applications.

Example 10 includes the subject matter of example 8, wherein thedetermination that a first behavior was performed by the firstapplication further comprises: comparing the one or more operations thatthe first application caused to occur with one or more known behaviorsusing a confidence score; and determining that the confidence scoreassociated with the comparison to the first behavior from among the oneor more known behaviors is above a threshold amount.

Example 11 includes the subject matter of example 10, wherein thedetermination that a first behavior was performed by the firstapplication further comprises comparing the one or more operations thatthe first application caused to occur with: one or more known behaviorsperformed on the first device; one or more known behaviors performed ondevices within the same enterprise as the first device; and one or moreknown behaviors performed on all devices monitored by the system.

Example 12 includes the subject matter of example 11, wherein theconfidence score is determined by independently weighting thecomparisons to the one or more known behaviors performed on: the firstdevice; devices within the same enterprise as the first device; and alldevices monitored by the system.

Example 13 includes the subject matter of example 8, wherein a phenotypeis identified each time an application is launched.

Example 14 includes the subject matter of example 8, wherein the memoryis in a location remote to the first device.

Example 15 is at least one non-transitory machine-readable storagemedium, comprising computer-executable instructions carried on thecomputer readable medium, the instructions readable by a processor, theinstructions, when read and executed, for causing the processor to:determine, based on one or more operations that a first applicationcaused to occur on a first device, that a first behavior was performedby the first application; compare the first behavior to a phenotype forthe first application; compare the first behavior to a phenotype for oneor more trusted applications, wherein the phenotype for a trustedapplication comprises one or more behaviors identified for therespective trusted application, and wherein the one or more trustedapplications are different applications from the first application;compare the first behavior to a phenotype for one or more known malwareapplications, wherein the phenotype for a known malware applicationcomprises one or more behaviors identified for the respective knownmalware application, and wherein the one or more known malwareapplications are different applications from the first application; anddetermine whether the first behavior is indicative of malware based, atleast in part, on the comparisons of the first behavior to: thephenotype for the first application, the phenotype for the one or moretrusted applications, and the phenotype for the one or more knownmalware applications.

Example 16 includes the subject matter of example 15, whereindetermination whether the first behavior is indicative of malwarecomprises determining a confidence score based on the comparisons of thefirst behavior to: the phenotype for the first application, thephenotype for the one or more trusted applications, and the phenotypefor the one or more known malware applications.

Example 17 includes the subject matter of example 15, wherein theinstructions to determine that a first behavior was performed by thefirst application further comprise instructions to: compare the one ormore operations that the first application caused to occur with one ormore known behaviors using a confidence score; and determine that theconfidence score associated with the comparison to the first behaviorfrom among the one or more known behaviors is above a threshold amount.

Example 18 includes the subject matter of example 17, wherein theinstructions to determine that a first behavior was performed by thefirst application further comprise instructions to compare the one ormore operations that the first application caused to occur with: one ormore known behaviors performed on the first device; one or more knownbehaviors performed on devices within the same enterprise as the firstdevice; and one or more known behaviors performed on all devicesmonitored by the system.

Example 19 includes the subject matter of example 18, wherein theconfidence score is determined by executing instructions toindependently weight the comparisons to the one or more known behaviorsperformed on: the first device; devices within the same enterprise asthe first device; and all devices monitored by the system.

Example 20 includes the subject matter of example 15, wherein theinstructions further comprise instruction to identify a phenotype eachtime an application is launched.

Example 21 includes the subject matter of example 15, wherein the memoryis in a location remote to the first device.

Example 22 is a device, comprising: a memory; one or more processorsconfigured to execute instructions stored in the memory, theinstructions comprising: an event processor module, configured to:receive a plurality of collected events; identify one or more microstepsfrom among the plurality of collected events, each microstep comprisingan aggregation or sequence of collected events that represent ahigher-level function; identify one or more behaviors, each behaviorcomprising an aggregation or sequence of microsteps that represent anactivity performed by an application executing on the device; anddetermine whether each of the one or more identified behaviors are knownor unknown; and a policy enforcer module, configured to: receive the oneor more identified behaviors from the event processor module; anddetermine whether each of the one or more identified behaviors areindicative of malware based, at least in part, on: a phenotype of anapplication that caused the respective identified behavior to occur, aphenotype for one or more trusted applications; and a phenotype for oneor more known malware applications, wherein the phenotype of anapplication comprises one or more normal behaviors for the respectiveapplication.

Example 23 includes the subject matter of example 22, wherein the eventprocessor module is further configured to store unknown identifiedbehaviors in a secure storage location communicatively coupled to thedevice.

Example 24 includes the subject matter of example 22, further comprisingan event collector configured to monitor memory operations occurring onthe device.

Example 25 includes the subject matter of example 22, wherein the policyenforcer module is further configured to determine that an identifiedbehavior is indicative of malware when: the phenotype of the applicationthat caused the identified behavior to occur and the phenotype of theone or more trusted applications do not comprise the identifiedbehavior; or the phenotype of the one or more known malware applicationsdoes comprise the identified behavior.

Example 26 is a computer system comprising: means for performing themethod of any one of claims 8-14.

The above description is intended to be illustrative, and notrestrictive. For example, the above-described embodiments may be used incombination with each other. Many other embodiments will be apparent tothose of skill in the art upon reviewing the above description. Thescope of the invention therefore should be determined with reference tothe appended claims, along with the full scope of equivalents to whichsuch claims are entitled.

What is claimed is:
 1. An application phenotyping system, theapplication phenotyping system comprising: one or more processors; and amemory including instructions that, when executed, cause the one or moreprocessors to: determine two or more first-tier rules, respective onesof the two or more first-tier rules referencing a corresponding sequenceof two or more operations to be performed by one or more applications tobe executed on one or more devices; identify second-tier rules,respective ones of the second-tier rules including a correspondingsequence of at least two of the first-tier rules; identify one or morebehaviors including a third sequence of at least two of the second-tierrules, the one or more behaviors to represent one or more normalactivities to be performed by the one or more applications; identify aphenotype for the one or more applications, the phenotype including oneor more of the behaviors, the phenotype to represent the one or more ofthe normal activities identified for the one or more applications; anddistribute an executable to the one or more devices, the executable tocause one or more of the devices to access the phenotype to identifymalware.
 2. The application phenotyping system of claim 1, wherein theexecutable is to cause at least one of the one or more devices todetermine a confidence score by comparing a first behavior of a firstapplication of the at least one device to at least one of: the phenotypefor the first application, the phenotype for one or more trustedapplications, or the phenotype for one or more known malwareapplications.
 3. The application phenotyping system of claim 1, whereina first device of the one or more devices includes a malware detectionsystem to execute the executable to identify the malware.
 4. Theapplication phenotyping system of claim 1, wherein the one or moreprocessors are to distribute the executable to a malware detectionsystem communicatively coupled to a first device of the one or moredevices over a network.
 5. The application phenotyping system of claim1, wherein the executable is to cause at least one of the one or moredevices to associate a first behavior with a first application by:comparing a fourth sequence of at least two of the two or moreoperations that the first application caused to occur with the one ormore behaviors to determine a confidence score; and determining that theconfidence score satisfies a threshold.
 6. The application phenotypingsystem of claim 5, wherein the executable is to compare the at least twoof the two or more operations with: the one or more behaviors to beperformed on a first device of the one or more devices; the one or morebehaviors to be performed on the one or more devices within a sameenterprise as the first device; and the one or more behaviors to beperformed on the one or more devices monitored by the applicationphenotyping system.
 7. The application phenotyping system of claim 6,wherein the confidence score is determined by independently weightingthe comparisons of the at least two of the two or more operations, and,the one or more behaviors are to be performed on: the first device; theone or more devices within the same enterprise as the first device; andall devices monitored by the system.
 8. A method for performing malwaredetection, the method comprising: determining two or more first-tierrules, respective ones of the two or more first-tier rules referencing acorresponding sequence of two or more operations to be performed by oneor more applications to be executed on one or more devices; identifyingsecond-tier rules, respective ones of the second-tier rules including acorresponding sequence of at least two of the first-tier rules;identifying one or more behaviors including a third sequence of at leasttwo of the second-tier rules, the one or more behaviors to represent oneor more normal activities to be performed by the one or moreapplications; identifying a phenotype for the one or more applications,the phenotype including one or more of the behaviors, the phenotype torepresent one or more of the normal activities for the one or moreapplications; and distributing an executable to the one or more devices,the executable to cause one or more of the devices to access thephenotype to identify malware.
 9. The method of claim 8, wherein theexecutable is to cause a first device of the one or more devices todetermine a confidence score by comparing a first behavior of a firstapplication of the first device to at least one of: the phenotype forthe first application, the phenotype for one or more trustedapplications, or the phenotype for one or more known malwareapplications.
 10. The method of claim 8, wherein the executable is tocause a first device of the one or more devices to associate a firstbehavior with a first application by: comparing a fourth sequence of atleast two of the two or more operations that the first applicationcaused to occur with the one or more behaviors to generate a confidencescore; and determining that the confidence score satisfies a threshold.11. The method of claim 10, wherein the executable is to cause the firstdevice to compare the at least two of the two or more operations with:the one or more behaviors to be performed on the first device; the oneor more behaviors to be performed on the one or more devices within asame enterprise as the first device; and the one or more behaviors to beperformed on second devices, the second devices including the firstdevice and the one or more devices within the same enterprise as thefirst device.
 12. The method of claim 11, wherein the confidence scoreis determined by independently weighting the comparisons of the at leasttwo of the two or more operations, and, the one or more behaviors to beperformed on: the first device; the one or more devices within the sameenterprise as the first device; and all devices monitored by a system.13. The method of claim 8, wherein the phenotype for a first applicationof the one or more applications is accessed each time the firstapplication is launched.
 14. The method of claim 8, wherein theexecutable is to cause a first device of the one or more devices toaccess the phenotype from a location remote to the one or more devices.15. At least one storage disk or storage device, comprising instructionsthat, when executed, cause one or more processors of a first device toat least: determine two or more first-tier rules, respective ones of thetwo or more first-tier rules referencing a corresponding sequence of twoor more operations that a first application caused to occur on the firstdevice; determine one or more second-tier rules, respective ones of theone or more second-tier rules referencing a corresponding sequence oftwo or more of the two or more first-tier rules; determine a firstbehavior; when the first behavior is not associated with one or morebehaviors associated with one or more normal activities to be performedby the first application, compare the first behavior to a phenotype forone or more known malware applications, the first behavior correspondingto a sequence of at least two of the second-tier rules; determinewhether the first behavior is indicative of malware based on thecomparison of the first behavior to the phenotype for the one or moreknown malware applications; and perform a corrective action on the firstdevice when the first behavior is indicative of malware.
 16. The atleast one storage disk or storage device of claim 15, wherein theinstructions cause the one or more processors to determine whether thefirst behavior is indicative of malware by determining a confidencescore by comparing the first behavior to at least one of: the phenotypefor the first application, the phenotype for one or more trustedapplications, or the phenotype for the one or more known malwareapplications.
 17. The at least one storage disk or storage device ofclaim 15, further including instructions to cause the one or moreprocessors to determine whether the first behavior is associated withthe one or more normal activities to be performed by the firstapplication by: comparing corresponding sequences of the two or moreoperations that the first application caused to occur with the one ormore behaviors to generate a confidence score; and determining whetherthe confidence score satisfies a threshold.
 18. The at least one storagedisk or storage device of claim 17, wherein the instructions cause theone or more processors to compare at least one of the correspondingsequences of the two or more operations with: the one or more behaviorsperformed on the first device; the one or more behaviors performed onsecond devices within a same enterprise as the first device; and the oneor more behaviors performed on third devices monitored by a malwaredetection system, the third devices including the first device and thesecond devices.
 19. The at least one storage disk or storage device ofclaim 18, wherein the confidence score is determined by independentlyweighting the comparisons of at least one of the corresponding sequencesof the two or more operations to the one or more behaviors to beperformed on: the first device; the second devices; and the thirddevices.
 20. The at least one storage disk or storage device of claim15, wherein the instructions cause the one or more processors to accessthe phenotype at launch of the first application.
 21. The at least onestorage disk or storage device of claim 15, wherein the instructionscause the one or more processors to access the phenotype from a locationremote to the first device.
 22. A device, comprising: one or moreprocessors; and a memory including instructions that, when executed,cause the one or more processors to: determine two or more first-tierrules, respective ones of the two or more first-tier rules referencing acorresponding sequence of two or more operations that a firstapplication caused to occur on the device; determine one or moresecond-tier rules, respective ones of the one or more second-tier rulesreferencing a corresponding sequence of two or more of the first-tierrules; determine a first behavior; when the first behavior is notassociated with one or more behaviors associated with one or more normalactivities to be performed by the first application, compare the firstbehavior to a phenotype for one or more known malware applications, thefirst behavior corresponding to a sequence of at least two of thesecond-tier rules; determine whether the first behavior is indicative ofmalware based on the comparison of the first behavior to the phenotypefor the one or more known malware applications; and perform a correctiveaction on the device when the first behavior is indicative of malware.23. The device of claim 22, wherein the one or more processors are tostore data representative of the first behavior in a secure storagelocation communicatively coupled to the device when the first behavioris not associated with the one or more behaviors.
 24. The device ofclaim 22, wherein the one or more processors are to monitor memoryoperations occurring on the device to identify the two or moreoperations.
 25. The device of claim 22, wherein the phenotype is a firstphenotype and the one or more processors are to determine that the firstbehavior is indicative of malware when: a second phenotype of the firstapplication that caused the first behavior to occur and a thirdphenotype of one or more trusted applications do not include the firstbehavior; or the first phenotype of the one or more known malwareapplications include the first behavior.